Update app.py

app.py

@@ -26,6 +26,7 @@ import pprint
 import json
 from huggingface_hub import HfApi, login, upload_folder, create_repo
 import os
+import requests
 
 # Load configuration file 
 with open('config.json', 'r') as config_file:
@@ -65,7 +66,7 @@ if (should_train_model=='1'): #train model
     #settings
     model_save_path = path_to_save_trained_model_to
     bias_non_fleet = 1.0
-    epochs_to_run = .1
+    epochs_to_run = .01
 
     file_path_train = train_file + ".csv"
     file_path_test = test_file + ".csv"
@@ -316,14 +317,22 @@ if (should_train_model=='1'): #train model
         commit_message="Push tokenizer",
         #overwrite=True  # Force overwrite existing files
     )
+
+    url = "http://210.1.253.35:200/api/hello"  # Example API
+    response = requests.get(url)
     
-else:
-    print('Load Pre-trained')
-    model_save_path = f"./{model_save_path}_model"
-    tokenizer_save_path = f"./{model_save_path}_tokenizer"
-    # RobertaTokenizer.from_pretrained(model_save_path)
-    model = AutoModelForSequenceClassification.from_pretrained(model_save_path).to('cpu')
-    tokenizer = AutoTokenizer.from_pretrained(tokenizer_save_path)
+    if response.status_code == 200:
+        data = response.json()  # Convert response to JSON
+        print(data)
+    else:
+        print(f"Error: {response.status_code}")
+    else:
+        print('Load Pre-trained')
+        model_save_path = f"./{model_save_path}_model"
+        tokenizer_save_path = f"./{model_save_path}_tokenizer"
+        # RobertaTokenizer.from_pretrained(model_save_path)
+        model = AutoModelForSequenceClassification.from_pretrained(model_save_path).to('cpu')
+        tokenizer = AutoTokenizer.from_pretrained(tokenizer_save_path)
 
 #Define the label mappings (this must match the mapping used during training)
 label_mapping = model.config.label_mapping